Price modeling using a variety of analytical methods has been described in many publications including, for example, economic treatises, textbooks, and patents. Numerical information culled from vast data sets containing numerous transaction based sales operations may be analyzed and displayed in any of a number of different ways such as through word processing, spreadsheets, and graphical software programs. Modeling this numerical information visually in a price modeling context presents various challenges to designers and analysts.
In order to effectively communicate via visual display methods information gleaned from data sets, designers face many challenges. In one instance the sheer number of data entries in a transaction based data warehouse can typically exceed many millions of transactions. Displaying millions of individual entries on a single display will not generally provide an analyst with useful information. That is, simply graphing a large number of entries alone will not generally provide useful insight into the characteristic and nature of the data set. In this situation, predictive analysis of the data becomes difficult, if not impossible, as a practical matter. Thus, designers must, in some fashion, aggregate, or allow the user to flexibly aggregate the data in order to display any meaningful characterization of a data set.
Typically, generally known statistical analysis methods may be employed to aggregate data. Averages, medians, modes, and other statistical methods well known in the art may be utilized to aggregate data so that trends and analysis may be affected. However, in some cases, unique methods of data aggregation may be desirable. For example, an average value of price quotes for a particular region may give a sales person a basis upon which to make a real time price quote to a customer. The average value, in this example, represents a statistical locus around which all the price quotes for that region tend to fall. Averaging has an added benefit of reducing a chosen set of data to a single number thus deriving performance gains when analyzing and manipulating the data set. However, as can be appreciated by one skilled in the art, average values cannot generally account for relative comparisons between groups of related items. To make a relative comparison between groups of related items, an index may be calculated.
Indexes, which are generally known in the art, have been employed in a variety of manners. Stock markets, for example, often use indexes as a gauge of general market condition. Index calculations are typically performed in batch processes. As such, indexes are indicators of past performance only and generally cannot be used to compare real-time data changes. Indexing also tends to aggregate data in ways that make it difficult if not impossible to explore lower level data orders such as individual transactions for example. Further, while indexes generally provide a single value as an indicator, they are not generally visualized in other ways. Thus, an innovative index calculation that may be generated in response to real-time data changes, that allows extraction and manipulation of the underlying data, and that may be visualized in new ways is desirable to achieve a more robust and rich analysis.
Another complication confronting designers in modeling and displaying large transaction based data sets is that in typical legacy systems, aggregating data often results in the loss of individual transaction information due in part to methodologies selected to enhance performance. As noted above, an average value represents a statistical locus around which all values in the data tend to fall. Also noted above, averaging represents a way of reducing a data set. In many cases, however, a finer level of granularity with respect to the data set may be desirable since, in at least some instances, outliers and dispersions of data tend to become aggregated or averaged away resulting in a distortion of original data set. Thus, for example, a data set may be reduced by averaging based on a given criteria (e.g., average prices for a given item by region). The data set, in this example, may be reduced to a single entry for each item in a given region. As an example, the average price of a widget in the western region may be represented as a single entry. Unfortunately, however, once a data set is reduced, the ability to examine a single transaction or even a group of transactions within the data set is typically severely limited or impossible. Thus, methods to recapture and display single or group transactions of a reduced data set may be desirable.
In view of the foregoing, systems and methods for generating and displaying indexed price modeling data.